• Journal of the Society of Disaster Information
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• v.17 no.4
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• pp.694-709
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• 2021

Purpose: In this study, earthquake-induced landslide risk assessment was conducted to provide basic data for efficient and preemptive damage prevention by selecting the erosion control work before the earthquake and the prediction and restoration priorities of the damaged area after the earthquake. Method: The study analyzed the previous studies abroad to examine the evaluation methodology and to derive the evaluation factors, and examine the utilization of the landslide hazard map currently used in Korea. In addition, the earthquake-induced landslide hazard map was also established on a pilot basis based on the fault zone and epicenter of Pohang using seismic attenuation. Result: The earthquake-induced landslide risk assessment study showed that China ranked 44%, Italy 16%, the U.S. 15%, Japan 10%, and Taiwan 8%. As for the evaluation method, the statistical model was the most common at 59%, and the physical model was found at 23%. The factors frequently used in the statistical model were altitude, distance from the fault, gradient, slope aspect, country rock, and topographic curvature. Since Korea's landslide hazard map reflects topography, geology, and forest floor conditions, it has been shown that it is reasonable to evaluate the risk of earthquake-induced landslides using it. As a result of evaluating the risk of landslides based on the fault zone and epicenter in the Pohang area, the risk grade was changed to reflect the impact of the earthquake. Conclusion: It is effective to use the landslide hazard map to evaluate the risk of earthquake-induced landslides at the regional scale. The risk map based on the fault zone is effective when used in the selection of a target site for preventive erosion control work to prevent damage from earthquake-induced landslides. In addition, the risk map based on the epicenter can be used for efficient follow-up management in order to prioritize damage prevention measures, such as to investigate the current status of landslide damage after an earthquake, or to restore the damaged area.

• Journal of Korean Library and Information Science Society
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• v.53 no.3
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• pp.73-94
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• 2022

Florence is the cradle of the Italian Renaissance. It is the result of a combination of medieval humanists' exploration of ancient Greek and Roman knowledge and culture, the leadership of great monarchs and priests, patronage of the Medici family, etc., free-thinking and creativity of artists, and critical consciousness and cultural needs of citizens. However, the Florentine Renaissance could not have blossomed unless the Medici family had collected ancient manuscripts and translations, and built libraries to preserve and provide literature. Based on this logical basis, this study outlined the Florentine renaissance and historic libraries, analyzed the collection and composition of favorite books of the Medici family, and traced the architectural characteristics and metaphors of the Medici libraries, The San Marco Library (Michelozzo Library), Library of Badia Fiesolana, and the San Lorenzo Library (Laurentian Library) were the priming and birthplace of the Florentine Renaissance despite of many difficulties, including earthquake, fire, restoration, transfer, seizure, and closure. In particular, the San Marco Library, which was opened in 1444 based on the financial support of Cosimo de' Medici, Michelozzo's design, and Niccoli's private collections was the first common library in the Renaissance period. And the architectural highlight of the Laurentian Library, which opened in 1571 under the leadership of Giulio (Papa Clemente VII), is Michelangelo's staircase, which symbolizes 'from ignorance to wisdom', and the real value of the content is the ancient manuscripts and early printed books, which were collected by the humanist Niccoli and the Medici family. In short, when discussing the Florentine Renaissance, Medici's collection and historic libraries are very important points. The reason is that the ancient collections were not stuffed products, but syntactic semiotics, and the libraries are telescopes that view the history of human knowledge and culture and microscopes that create knowledge and wisdom. If records dominate memories, libraries accumulate records. Therefore, long breathing and time capsule strategies are also required for the development and preservation of retroactive books in domestic libraries with a relatively long history.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.1-25
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• 2023

ESG management is becoming a necessity of the times, but there are about 600 ESG evaluation indicators worldwide, causing confusion in the market as different ESG ratings were assigned to individual companies according to evaluation agencies. In addition, since the method of applying ESG was not disclosed, there were not many ways for companies that wanted to introduce ESG management to get help. Accordingly, the Ministry of Trade, Industry and Energy announced the K-ESG guideline jointly with the ministries. In previous studies, there were few studies on the comparison of evaluation grades by ESG evaluation company or the application of evaluation diagnostic items. Therefore, in this study, the ease of application and improvement of the K-ESG guideline was attempted by applying the K-ESG guideline to companies that already have ESG ratings. The position of the K-ESG guideline is also confirmed by comparing the scores calculated through the K-ESG guideline for companies that have ESG ratings from global ESG evaluation agencies and domestic ESG evaluation agencies. As a result of the analysis, first, the K-ESG guideline provide clear and detailed standards for individual companies to set their own ESG goals and set the direction of ESG practice. Second, the K-ESG guideline is suitable for domestic and global ESG evaluation standards as it has 61 diagnostic items and 12 additional diagnostic items covering the evaluation indicators of global representative ESG evaluation agencies and KCGS in Korea. Third, the ESG rating of the K-ESG guideline was higher than that of a global ESG rating company and lower than or similar to that of a domestic ESG rating company. Fourth, the ease of application of the K-ESG guideline is judged to be high. Fifth, the point to be improved in the K-ESG guideline is that the government needs to compile industry average statistics on diagnostic items in the K-ESG environment area and publish them on the government's ESG-only site. In addition, the applied weights of E, S, and G by industry should be determined and disclosed. This study will help ESG evaluation agencies, corporate management, and ESG managers interested in ESG management in establishing ESG management strategies and contributing to providing improvements to be referenced when revising the K-ESG guideline in the future.

• Journal of Nutrition and Health
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• v.56 no.6
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• pp.667-682
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• 2023

Purpose: This study aimed to investigate the changes in health behavior, mental health, and nutritional status of Korean adolescents before and after the coronavirus disease 2019 (COVID-19) pandemic outbreak. Methods: A total of 800 adolescents (12~18 years old) who participated in the 2019-2020 Korea National Health and Nutrition Examination Survey (KNHANES) were included as study subjects and divided into four groups (204 middle school boys, 172 middle school girls, 219 high school boys, and 205 high school girls). The 2019 and 2020 KNHANES data were classified into data collected before and after the COVID-19 outbreak, respectively. Results: After the COVID-19 pandemic outbreak, middle school boys showed an increased tendency toward becoming overweight and obese, with significantly increased levels of diastolic blood pressure and insulin. While there was no major change in the subjective health status among adolescents, the high school boys showed a significantly decreased physical activity after COVID-19 outbreak. Moreover, the proportion of middle school students feeling a little stressed significantly increased after the COVID-19 outbreak. The rate of skipping breakfast significantly increased in middle school girls, but the rate of having lunch with companions significantly increased among all adolescents after the COVID-19 outbreak. However, the intake of milk, vegetables, fruits, seaweeds, and pulses significantly decreased, although the intake of sugars, beverages, and seasonings significantly decreased as well, during this period. These changes may lead to an increased proportion of adolescents with insufficient intake of nutrients, including potassium, vitamin C, and riboflavin. Conclusion: These results highlight the impact of COVID-19 on comprehensive changes in physical and mental health status, lifestyle behavior, and nutritional status in adolescents, suggesting the need for targeted prevention and intervention for physical and mental well-being during the pandemic.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1615-1633
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• 2023

Sargassum horneri is one of the floating algae in the sea, which breeds in large quantities in the Yellow Sea and East China Sea and then flows into the coast of Republic of Korea, causing various problems such as destroying the environment and damaging fish farms. In order to effectively prevent damage and preserve the coastal environment, the development of Sargassum horneri detection algorithms using satellite-based remote sensing technology has been actively developed. However, incorrect detection information causes an increase in the moving distance of ships collecting Sargassum horneri and confusion in the response of related local governments or institutions,so it is very important to minimize false detections when producing Sargassum horneri spatial information. This study applied technology to automatically remove false detection results using the GOCI-II-based Sargassum horneri detection algorithm of the National Ocean Satellite Center (NOSC) of the Korea Hydrographic and Oceanography Agency (KHOA). Based on the results of analyzing the causes of major false detection results, it includes a process of removing linear and sporadic false detections and green algae that occurs in large quantities along the coast of China in spring and summer by considering them as false detections. The technology to automatically remove false detection was applied to the dates when Sargassum horneri occurred from February 24 to June 25, 2022. Visual assessment results were generated using mid-resolution satellite images, qualitative and quantitative evaluations were performed. Linear false detection results were completely removed, and most of the sporadic and green algae false detection results that affected the distribution were removed. Even after the automatic false detection removal process, it was possible to confirm the distribution area of Sargassum horneri compared to the visual assessment results, and the accuracy and precision calculated using the binary classification model averaged 97.73% and 95.4%, respectively. Recall value was very low at 29.03%, which is presumed to be due to the effect of Sargassum horneri movement due to the observation time discrepancy between GOCI-II and mid-resolution satellite images, differences in spatial resolution, location deviation by orthocorrection, and cloud masking. The results of this study's removal of false detections of Sargassum horneri can determine the spatial distribution status in near real-time, but there are limitations in accurately estimating biomass. Therefore, continuous research on upgrading the Sargassum horneri monitoring system must be conducted to use it as data for establishing future Sargassum horneri response plans.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.65-72
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• 2024

To faithfully represent the intention of the work in the exhibition space, lighting that provides high color reproduction like natural light is required. Thus, many lighting technologies have been introduced to improve CRI, but most of them only evaluated the general color rendering index (CRI Ra), which considers eight pastel colors. Natural light provides excellent color rendering performance for all colors, including red and blue, expressed by color rendering index of R9 and R12, but most artificial lighting has the problem that color rendering performance such as R9 and R12 is significantly lower than that of natural light. Recently, lighting technology that provides CRI at the level of natural light is required to realistically express the colors of works including primary colors but related research is very insufficient. Therefore this paper proposes exhibition hall lighting that fulfills CRI with a focus on CRI Ra, R9, and R12 based on the characteristics of natural light. First reinforcement wavelength bands for improving R9 and R12 are selected through analysis of the actual measurement SPD of natural and artificial lighting. Afterward virtual SPDs with a peak wavelength within the reinforcement wavelength band are created and then SPD combination conditions that satisfy CRI Ra≥95, R9, and R12≥90 are derived through combination simulation with a commercial LED light source. Through this, after specifying two types of light sources with 405,630nm peak wavelength that had the greatest impact on the improvement of R9 and R12, the exhibition hall lighting applied with two W/C White LEDs is designed and a control Index DB of the lighting is constructed. Afterward experiments with the proposed method showed that it was possible to achieve high CRI at the level of natural light with average CRI Ra 96.5, R9 96.2, and R12 94.0 under the conditions of illuminance (300-1,000 Lux) and color temperature (3,000-5,000K).

• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.5
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• pp.27-41
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• 2024

The current method of calculating the carbon stocks of Korea's forests is to multiply the forest standing crop by basic wood density, biomass expansion factor, and carbon fraction, but it does not sufficiently reflect forest vegetation. This study attempted to present a method of calculating carbon stocks using the biotope map and Biomass Allometric Equations for forests in Suji-gu, Yongin City. The biotope map is prepared every five years and contains detailed information on vegetation, including the actual vegetation and land cover status. The forest biotope of Suji-gu was extracted from the Yongin City biotope map, and the tree species, height, and breast height diameter of 24 representative types of forest vegetation sampled in Yongin City were analyzed in detail. To calculate the carbon stocks of trees and shrubs, the Biomass Allometric Equations developed by the National Institute of Forest Science was used, and to calculate the carbon stocks of shrubs, the previous research result of 0.457 kg per m² was applied. First, carbon storage was calculated for each types of forest vegetation sampled in Yongin City, and in order to apply this to the entire area, the 125 forest vegetation types in Suji-gu, Yongin City were retyped into 50. As a result, the Quercus mongolica community occupied the largest area, followed by the Pinus rigida community, the Quercus acutissima community, and the Quercus serrata community. The community with the highest carbon stocks per unit area (m²) was the Cornus controversa-Quercus mongolica community, and the community with the lowest was the oak-Betula dahurica community. The carbon stocks amount of forests in Suji-gu, Yongin City, calculated by applying the biotope map and Biomass Allometric Equations, was relatively higher than the carbon stocks amount calculated by multiplying existing forest standing crop by basic wood density, biomass expansion factor, and carbon fraction. This is because the currently officially used forest standing crop in Yongin City (144.44 m³/ha) does not sufficiently reflect the actual forest vegetation, and trees with a breast height diameter of less than 6 cm were excluded when surveying forest standing crop, resulting in a lower carbon stocks amount than the actual amount. This study complements the limitations of existing carbon stocks calculation methods by utilizing a biotope map with detailed information on vegetation, such as existing vegetation maps and land cover status, and a Biomass Allometric Equations developed by the National Institute of Forest Science, and provides higher precision when assessing carbon stocks of forests. It is meaningful in suggesting a method for calculating carbon stocks.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.167-181
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• 2018

Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN(Convolutional Neural Network), which is known as the effective solution for recognizing and classifying images or voices, has been popularly applied to classification and prediction problems. In this study, we investigate the way to apply CNN in business problem solving. Specifically, this study propose to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. As mentioned, CNN has strength in interpreting images. Thus, the model proposed in this study adopts CNN as the binary classifier that predicts stock market direction (upward or downward) by using time series graphs as its inputs. That is, our proposal is to build a machine learning algorithm that mimics an experts called 'technical analysts' who examine the graph of past price movement, and predict future financial price movements. Our proposed model named 'CNN-FG(Convolutional Neural Network using Fluctuation Graph)' consists of five steps. In the first step, it divides the dataset into the intervals of 5 days. And then, it creates time series graphs for the divided dataset in step 2. The size of the image in which the graph is drawn is $40(pixels){\times}40(pixels)$, and the graph of each independent variable was drawn using different colors. In step 3, the model converts the images into the matrices. Each image is converted into the combination of three matrices in order to express the value of the color using R(red), G(green), and B(blue) scale. In the next step, it splits the dataset of the graph images into training and validation datasets. We used 80% of the total dataset as the training dataset, and the remaining 20% as the validation dataset. And then, CNN classifiers are trained using the images of training dataset in the final step. Regarding the parameters of CNN-FG, we adopted two convolution filters ($5{\times}5{\times}6$ and $5{\times}5{\times}9$) in the convolution layer. In the pooling layer, $2{\times}2$ max pooling filter was used. The numbers of the nodes in two hidden layers were set to, respectively, 900 and 32, and the number of the nodes in the output layer was set to 2(one is for the prediction of upward trend, and the other one is for downward trend). Activation functions for the convolution layer and the hidden layer were set to ReLU(Rectified Linear Unit), and one for the output layer set to Softmax function. To validate our model - CNN-FG, we applied it to the prediction of KOSPI200 for 2,026 days in eight years (from 2009 to 2016). To match the proportions of the two groups in the independent variable (i.e. tomorrow's stock market movement), we selected 1,950 samples by applying random sampling. Finally, we built the training dataset using 80% of the total dataset (1,560 samples), and the validation dataset using 20% (390 samples). The dependent variables of the experimental dataset included twelve technical indicators popularly been used in the previous studies. They include Stochastic %K, Stochastic %D, Momentum, ROC(rate of change), LW %R(Larry William's %R), A/D oscillator(accumulation/distribution oscillator), OSCP(price oscillator), CCI(commodity channel index), and so on. To confirm the superiority of CNN-FG, we compared its prediction accuracy with the ones of other classification models. Experimental results showed that CNN-FG outperforms LOGIT(logistic regression), ANN(artificial neural network), and SVM(support vector machine) with the statistical significance. These empirical results imply that converting time series business data into graphs and building CNN-based classification models using these graphs can be effective from the perspective of prediction accuracy. Thus, this paper sheds a light on how to apply deep learning techniques to the domain of business problem solving.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.169-196
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• 2017

The fourth industrial revolution represents a revolutionary change in the business environment and its ecosystem, which is a fusion of Information Technology (IT) and other industries. In line with these recent changes, the Ministry of Employment and Labor of South Korea announced 'the Fourth Industrial Revolution Leader Training Program,' which includes five key support areas such as (1) smart manufacturing, (2) Internet of Things (IoT), (3) big data including Artificial Intelligence (AI), (4) information security, and (5) bio innovation. Based on this program, we can get a glimpse of the South Korean government's efforts and willingness to emit leading human resource with advanced IT knowledge in various fusion technology-related and newly emerging industries. On the other hand, in order to nurture excellent IT manpower in preparation for the fourth industrial revolution, the role of educational institutions capable of providing high quality IT education services is most of importance. However, these days, most IT educational institutions have had difficulties in providing customized IT education services that meet the needs of consumers (i.e., learners), without breaking away from the traditional framework of providing supplier-oriented education services. From previous studies, it has been found that the provision of customized education services centered on learners leads to high satisfaction of learners, and that higher satisfaction increases not only task performance and the possibility of business application but also learners' recommendation intention. However, since research has not yet been conducted in a comprehensive way that consider both antecedent and consequent factors of the learner's satisfaction, more empirical research on this is highly desirable. With the advent of the fourth industrial revolution, a rising interest in various convergence technologies utilizing information technology (IT) has brought with the growing realization of the important role played by IT-related education services. However, research on the role of IT education service quality in the context of IT education is relatively scarce in spite of the fact that research on general education service quality and satisfaction has been actively conducted in various contexts. In this study, therefore, the five dimensions of IT education service quality (i.e., tangibles, reliability, responsiveness, assurance, and empathy) are derived from the context of IT education, based on the SERVPERF model and related previous studies. In addition, the effects of these detailed IT education service quality factors on learners' educational satisfaction and their work application/recommendation intentions are examined. Furthermore, the moderating roles of learner position (i.e., practitioner group vs. manager group) and participation motivation (i.e., voluntary participation vs. involuntary participation) in relationships between IT education service quality factors and learners' educational satisfaction, work application intention, and recommendation intention are also investigated. In an analysis using the structural equation model (SEM) technique based on a questionnaire given to 203 participants of IT education programs in an 'M' IT educational institution in Seoul, South Korea, tangibles, reliability, and assurance were found to have a significant effect on educational satisfaction. This educational satisfaction was found to have a significant effect on both work application intention and recommendation intention. Moreover, it was discovered that learner position and participation motivation have a partial moderating impact on the relationship between IT education service quality factors and educational satisfaction. This study holds academic implications in that it is one of the first studies to apply the SERVPERF model (rather than the SERVQUAL model, which has been widely adopted by prior studies) is to demonstrate the influence of IT education service quality on learners' educational satisfaction, work application intention, and recommendation intention in an IT education environment. The results of this study are expected to provide practical guidance for IT education service providers who wish to enhance learners' educational satisfaction and service management efficiency.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.55-79
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• 2013

In this paper, we propose a hybrid genetic algorithm (HGA) approach to effectively solve the reverse logistics network with centralized centers (RLNCC). For the proposed HGA approach, genetic algorithm (GA) is used as a main algorithm. For implementing GA, a new bit-string representation scheme using 0 and 1 values is suggested, which can easily make initial population of GA. As genetic operators, the elitist strategy in enlarged sampling space developed by Gen and Chang (1997), a new two-point crossover operator, and a new random mutation operator are used for selection, crossover and mutation, respectively. For hybrid concept of GA, an iterative hill climbing method (IHCM) developed by Michalewicz (1994) is inserted into HGA search loop. The IHCM is one of local search techniques and precisely explores the space converged by GA search. The RLNCC is composed of collection centers, remanufacturing centers, redistribution centers, and secondary markets in reverse logistics networks. Of the centers and secondary markets, only one collection center, remanufacturing center, redistribution center, and secondary market should be opened in reverse logistics networks. Some assumptions are considered for effectively implementing the RLNCC The RLNCC is represented by a mixed integer programming (MIP) model using indexes, parameters and decision variables. The objective function of the MIP model is to minimize the total cost which is consisted of transportation cost, fixed cost, and handling cost. The transportation cost is obtained by transporting the returned products between each centers and secondary markets. The fixed cost is calculated by opening or closing decision at each center and secondary markets. That is, if there are three collection centers (the opening costs of collection center 1 2, and 3 are 10.5, 12.1, 8.9, respectively), and the collection center 1 is opened and the remainders are all closed, then the fixed cost is 10.5. The handling cost means the cost of treating the products returned from customers at each center and secondary markets which are opened at each RLNCC stage. The RLNCC is solved by the proposed HGA approach. In numerical experiment, the proposed HGA and a conventional competing approach is compared with each other using various measures of performance. For the conventional competing approach, the GA approach by Yun (2013) is used. The GA approach has not any local search technique such as the IHCM proposed the HGA approach. As measures of performance, CPU time, optimal solution, and optimal setting are used. Two types of the RLNCC with different numbers of customers, collection centers, remanufacturing centers, redistribution centers and secondary markets are presented for comparing the performances of the HGA and GA approaches. The MIP models using the two types of the RLNCC are programmed by Visual Basic Version 6.0, and the computer implementing environment is the IBM compatible PC with 3.06Ghz CPU speed and 1GB RAM on Windows XP. The parameters used in the HGA and GA approaches are that the total number of generations is 10,000, population size 20, crossover rate 0.5, mutation rate 0.1, and the search range for the IHCM is 2.0. Total 20 iterations are made for eliminating the randomness of the searches of the HGA and GA approaches. With performance comparisons, network representations by opening/closing decision, and convergence processes using two types of the RLNCCs, the experimental result shows that the HGA has significantly better performance in terms of the optimal solution than the GA, though the GA is slightly quicker than the HGA in terms of the CPU time. Finally, it has been proved that the proposed HGA approach is more efficient than conventional GA approach in two types of the RLNCC since the former has a GA search process as well as a local search process for additional search scheme, while the latter has a GA search process alone. For a future study, much more large-sized RLNCCs will be tested for robustness of our approach.